Signalomes: PhosR Signalomes

In PengyiYang/PhosR: A set of methods and tools for comprehensive analysis of phosphoproteomics data

Description

A function to generate signalomes

Usage

Signalomes(KSR, predMatrix, exprsMat, KOI, threskinaseNetwork=0.9,
signalomeCutoff=0.5)

Arguments

KSR	kinase-substrate relationship scoring results
predMatrix	output of kinaseSubstratePred function
exprsMat	a matrix with rows corresponding to phosphosites and columns corresponding to samples
KOI	a character vector that contains kinases of interest for which expanded signalomes will be generated
threskinaseNetwork	threshold used to select interconnected kinases for the expanded signalomes
signalomeCutoff	threshold used to filter kinase-substrate relationships

Value

A list of 3 elements. Signalomes, proteinModules and kinaseSubstrates

Examples

data('phospho_L6_ratio')
data('SPSs')

grps = gsub('_.+', '', colnames(phospho.L6.ratio))

# Cleaning phosphosite label
phospho.site.names = rownames(phospho.L6.ratio)
L6.sites = gsub(' ', '', sapply(strsplit(rownames(phospho.L6.ratio), '~'),
                                function(x){paste(toupper(x[2]), x[3], '',
                                                sep=';')}))
phospho.L6.ratio = t(sapply(split(data.frame(phospho.L6.ratio), L6.sites),
                            colMeans))
phospho.site.names = split(phospho.site.names, L6.sites)

# Construct a design matrix by condition
design = model.matrix(~ grps - 1)

# phosphoproteomics data normalisation using RUV
ctl = which(rownames(phospho.L6.ratio) %in% SPSs)
phospho.L6.ratio.RUV = RUVphospho(phospho.L6.ratio, M = design, k = 3,
                                ctl = ctl)

phosphoL6 = phospho.L6.ratio.RUV
rownames(phosphoL6) = phospho.site.names

# filter for up-regulated phosphosites
phosphoL6.mean <- meanAbundance(phosphoL6, grps = gsub('_.+', '',
                                colnames(phosphoL6)))
aov <- matANOVA(mat=phosphoL6, grps=gsub('_.+', '', colnames(phosphoL6)))
phosphoL6.reg <- phosphoL6[(aov < 0.05) &
                        (rowSums(phosphoL6.mean > 0.5) > 0),,drop = FALSE]
L6.phos.std <- standardise(phosphoL6.reg)
rownames(L6.phos.std) <- sapply(strsplit(rownames(L6.phos.std), '~'),
    function(x){gsub(' ', '', paste(toupper(x[2]), x[3], '', sep=';'))})

L6.phos.seq <- sapply(strsplit(rownames(phosphoL6.reg), '~'),
                    function(x)x[4])

L6.matrices <- kinaseSubstrateScore(PhosphoSite.mouse, L6.phos.std,
    L6.phos.seq, numMotif = 5, numSub = 1)
set.seed(1)
L6.predMat <- kinaseSubstratePred(L6.matrices, top=30)

kinaseOI = c('PRKAA1', 'AKT1')

Signalomes_results <- Signalomes(KSR=L6.matrices,
                                predMatrix=L6.predMat,
                                exprsMat=L6.phos.std,
                                KOI=kinaseOI)

PengyiYang/PhosR documentation built on June 21, 2020, 8:37 a.m.